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The paper presents a network based data acquisition system useful for various scientific experiments and monitoring of different process parameters. The system implementation details are reported in the paper. The hardware implementation incorporates the design and development of sensor nodes and display nodes required for the system. Software development includes the development of network control algorithm required for host PC along with the sensor and display nodes. For validating the system four sensor nodes and one display node are installed at different locations at different distances. The collected data, by the system, is also presented in the paper.

Data Acquisition System; RS485 Network; Hardware Development; Software Development; Sensor; Signal Conditioning.

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Nipan Das , Kunjalata Kalita, "Design and Development of a Network Based Data Acquisition System", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.98-103, 2016.

Speech is the most usual form of human communication and speech processing has been one of the most stimulating expanses of signal processing. Speech recognition is the process of automatically recognizing the spoken words of person based on information in speech signal. Speech technology and human computer interaction systems have witnessed a steady and significant improvement over the last two decades. Now – a - days, speech technologies are commercially available for an unlimited but exciting range of tasks. Using these technologies machines are able to respond correctly and dependably to human voices, and provide useful and valuable services. Recent research concentrates on developing systems that would be much more robust against unpredictability in environment, speaker and language. Hence today researchers mainly focus on Speech Recognition systems with a large vocabulary that support speaker independent operation with continuous speech in different languages.

Speech Recognition; Feature Extraction; MFCC; LPC; ANN; VQ; HMM

[1] Lawrence R. Rabiner, Ronald W. Schafer, “Introduction to Digital Speech Processing”, Foundations and Trends in Signal Processing, Vol. 1, Nos. 1–2 (2007), DOI: 10.1561/2000000001
[2] L. Rabiner and B. Juang, Fundamentals of Speech Recognition, Prentice-Hall, 1993
[3] Bhargab Medhi, Prof. P.H.Talukdar, “Assamese Speaker Recognition Using Artificial Neural Network”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, March 2015
[4] Mayur R Gamit, Kinnal Dhameliya, “ISOLATED WORDS RECOGNITION USING MFCC, LPC AND NEURAL NETWORK”, IJRET, eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 04 Issue: 06 | June-2015
[5] Shanthi Therese S., Chelpa Lingam, “Review of Feature Extraction Techniques in Automatic Speech Recognition”, International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) Volume No.2, Issue No.6,
[6] Nidhi Desai, Prof.Kinnal Dhameliya, Prof.Vijayendra Desai, “Feature Extraction and Classification Techniques for Speech Recognition: A Review”, International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 12, December 2013)
[7] Suman K. Saksamudre, R. R. Deshmukh , "Isolated Word Recognition System for Hindi Language", International Journal of Computer Sciences and Engineering, Volume-03, Issue-07, Page No (110-114), Jul -2015
[8] Shreya Narang, Ms. Divya Gupta, “Speech Feature Extraction Techniques: A Review”, International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 4, Issue. 3, March 2015, pg.107 – 114

Antara Chowdhury and Adarsh Pradhan , "Isolated Assamese words spoken by Male and Female speakers", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.104-109, 2016.

Digital Image Processing applies efficient computer algorithms to process an image in digital computer. Different distortions occurred in image due to various reasons in image acquition, preprocessing, compression, reproduction can be removed by applying different methods like reducing noise, improving contrast etc. Image quality estimation is very widely used for many applications related to medical grounds, security related issues etc. Image quality can be measured either by Objective or Subjective methods. Mostly Peak Signal- to-Noise Ratio, Mean Squared Error, Structural Similarity Index Metric are used to estimate the quality of image using full reference objective method. Only in a few areas no reference and reduced reference are used to estimate image quality. Herein, different image quality parameters along with the image quality metrics have been reviewed. A fish bone model is proposed for expressing different estimating techniques of image quality parameters.

Image Quality; Image Quality Estimation; Image Quality Measures; Image Quality Parameters

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[21]. Hartwig Fronthaler, Klaus Kollreider, Josef Bigun, Julian Fierrez-Aguilar, Fernando Alonso-Fernandez, Javier Ortega-Garcia, and Joaquin Gonzalez-Rodriguez, “Fingerprint Image Quality Estimation and its Application to Multi-Algorithm Verification”,2006
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[23]. Mohammed Hassan and Chakravarthy Bhagvati, “Structural Similarity Measure for Color Images”
[24]. V. Ramadevi, S. Poongodi, “Estimation of Video and Watermark Image Quality using Singular Value Decomposition”
[25]. Anjali Krishna, Shanavaz K T “Effective Image Quality Estimation Using Wavelet Based Watermarking Technique”
[26]. Peng Ye, Jayant Kumar, Le Kang, David Doermann, “Real-time No-Reference Image Quality Assessment based on Filter Learning”
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[30]. Shruti Ghorpade, Dhanashri Gund, Swapnada Kadam, Prof. Mr.R.A.Jamadar, “Image Quality Assessment for Fake Biometric Detection: Application to Face and Fingerprint Recognition”, January 2015
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Minakshi Gogoi and Mala Ahmed, "Image Quality Parameter Detection : A Study", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.110-116, 2016.

This paper proposes a fast and efficient indexing technique that can be used in an image indexing and retrieval system for voluminous image data. The proposed technique is based on K-d tree which uses multi-dimensional features. At first the colour feature of a set of images are extracted. Then an index tree is generated with K-d tree index based on these colour features. After indexing is done the efficiency of the method is tested against search time for the collected dataset. The validation of the method is also tested with and without indices for the said dataset.

Indexing; k-d tree; multi-dimensional feature; colour moment; haar wavelet

[1] Md. K. I. Rahmani, and R. Sharma, “Image Indexing and Retrieval,” International Journal of Software and Web Sciences (IJSWS), ISSN (Print): 2279-0063, ISSN (Online): 2279-0071.
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[3] Li, Jia and Wang, Cheng, “Indexing Method for Hyperspectral Data Fast Retrieval by Pyramid Technique,” International Conference on Computer Science and Software Engineering, 2008.
[4] U. Jayaraman, S. Prakash, and P. Gupta, “Indexing Multimodal Biometric Databases Using Kd-Tree with Feature Level Fusion.”
[5] M. Gogoi, and D. K. Bhattacharya, "An Effective Fingerprint Verification Technique," Journal of Computer Science and Engineering, Volume 1, Issue 1, May 2010.
[6] J. K Lawder, and P. J. H. King, “Querying Multi-dimensional Data Indexed Using the Hilbert Space-Filling Curve.”
[7] D. P. Tian, “ A Review on Image Feature Extraction and Representation Techniques ,” International Journal of Multimedia and Ubiquitous Engineering , Volume 8, No 4, July 2013.
[8] T. K. Shih, J. Y. Huang, C. S. Wang, et al., “An intelligent content-based image retrieval system based on colour, shape and spatial relations”, In Proc. National Science Council, R. O.C., Part A: Physical Science and Engineering, vol. 25, no. 4, pp. 232-243, 2001.
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Jayashree Das and Minakshi Gogoi, "Indexing of Voluminous Data Using K-D Tree with Reference to CBIR", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.117-124, 2016.

This study aims at identifying various Human Resources (HR) analytics as part of Business Intelligence (BI), for systematic and effective handling of HR issues. Further, this study identifies the importance ofBI in the field of Human Resource Management (HRM) through various established models of BI like SAP ERP Workforce Analytics, SAS Human Capital Predictive Analytics and Retention Modeling, Oracle Human Resources Analytics and IBM Cognos Business Intelligence. The study focuses on how these BI models helps in attaining sustainable business growth through the strategic alignment of HR issues with the overall business objectives. The study follows a descriptive approach. The published works (mainly from Emerald, Science Direct and EBSCO) are being reviewed to identify various HR analytics that contributes to the overall BI. The study concludes that HR analytics as a part of BI helps an organization in systematically handling Human Resources with the use of data management and warehousing technologies across different functions of HRM. Thus it leads to work force stability within the organization. On the other hand a stable work force leads to continuous business growth and helps an organization in its sustainability and thereby creates competitive advantage for the organization in its external business environment. The implication drawn out of this study states that HR analytics as a form of BI helps in simplifying HR functions and creates strategic alignment with the overall business objectives. The present study is a modest attempt to compile various existing studies in a meaningful direction in order to generate a need for the use of BI in the field of HRM.

HR analytics, Business intelligence, Data management, and Data warehousing

[1]. Boudreau, W. John &Peter, M. Ramstad, (2006). Talent ship and HR measurement and analysis: from ROI to strategic organizational change. Human Resource Planning, 29(1), 25
[2]. Chhinzer, Nita and Gurdeep,Ghatehorde. (2009). Challenging relationships: hr metrics and organizational financial performance. The Journal of Business Inquiry 8(1), 37-48. DOI: 10.4102.v8i1.276
[3]. Hota & Ghosh. (2013), Workforce analytics approach: an emerging trend of workforce management. Tenth AIMS International Conference on Management.
[4]. Jon, Ingham. (2011). Using a human capital scorecard as a framework for analytical discovery. Strategic HR Review Journal, 10(2), 24-29
[5]. Kapoor, B., &Sherif, J. (2012).Human resources in an enriched environment of business intelligence. Kybernetes, 41(10), 1625-1637.
[6]. Lawler, Edward E., Alec, Levenson., &John, W. Boudreau. (2004). HR metrics and analytics: use and impact .Human Resource Planning; 27(4), 27
[7]. Levenson, Alec, (2005). Harnessing the Power of HR Analytics” Strategic HR Review; 4(3), 28
[8]. Magau, M.D., & Roodt, G. (2010).An evaluation of the Human Capital Bridge TM framework.SA Journal of Human Resource Management, 8(1).
[9]. Marten et.al (2013), A Framework for Business Analytics in Performance Management International Journal of Productivity and Performance Management, 62(1), 110-122
[10]. Mayo, A. (2006). Measuring and reporting: The fundamental requirement for data. CIPD - Research Report. Retrieved from http://www.mayolearning.com/mayo-publications/

GitikaTalukdar, "Human Resources Analytics: An Approach Towards Business Intelligence", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.0-0, 2016.

In this work, various edge detection techniques have been implemented on an image of a leaf taken under different light conditions to study the venation pattern of that leaf. The efficiency and the accuracy of these techniques in detection of the veins have been compared and analyzed. Edge detection operators such as Sobel and Canny edge detectors have also been implemented in the leaf image to identify the difference between the two. Step edges and ridge edges have been found out by taking the Gaussian and the first and second order derivative of the Gaussian of the image. The experimental result showed that canny edge detectors have been more accurate and can detect the veins more precisely with more details as compared to other techniques.

Gradient, first order derivative, second order derivative, Gaussian of an image, Step edges

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[4] Nassir Salman, “Image Segmentation Based on Watershed and Edge Detection Techniques”, The International Arab Journal of Information Technology, Vol. 3, No. 2, April 2006
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Agnimitra Borah, "Study of Venation of leaf using Image Processing", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.130-135, 2016.

It is common practice to collect feedbacks from the students in order to evaluate the performance of their teachers. Questionnaire is one of the best ways of collecting this information. Analysis of this questionnaire is crucial and not easy especially if it contains both quantitative and qualitative measures. This paper provides a comprehensive analysis of students’ evaluation of faculties’ teaching Computer Science at GIMT. The 5-points scale being used in quantitative analysis is being converted to 2-points scale and is supplemented by result from the qualitative analysis.

mixed research method; students’ feedback; Likert scale

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Th. Shanta Kumar , "Extracting Patterns from Students’ Feedback using Mixed Method", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.136-139, 2016.

Extensive scaling in Conventional MOSFETs lead to degradation to their electrical parameters. This work proposes a GaN channel Nanoscale MOSFET for improvement in Electron Mobility, Off current with satisfactory On current, Threshold voltage and Subthreshold Swing Off current of the order of 10-11 A/um and Electron Mobility of around 1300 cm2/ V-s are obtained.

MOSFET, GaN, Silicon Germanium, On-Off current ratio, mobility

[1]. U. K. Mishra, Y.Wu, B. P. Keller, S. Keller and S. P. Denbaars, “GaN Microwave Electronics”, IEEE Trans. on Microwave Theory and Techniques, vol. 46, no.6, pp. 756–761, June 1998.
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[3]. R. Prasher, D. Dass and R. Vaid, “ Performance of a Double Gate Nanoscale MOSFET (DG-MOSFET) Based on Novel Channel Materials”, Journal of Nano- and Electronic Physics, vol. 5, pp. 01017-1 – 01017-5, March 2013
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Basab Das, "GaN channel Nanoscale MOSFET with Silicon Source and Drain and Silicon Germanium Bulk", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.140-143, 2016.

With the recent developments in sensors and instrumentation engineering, there is a growing application of optical fibre as sensor for the measurement of various parameters like pressure, temperature, liquid level, humidity, displacement, volume etc. Over the years, there has been a great deal of interest on the development of liquid level sensors using fibre optics. Measurement of liquid level is important for various industrial and laboratory applications and fibre optic sensors are ideal for such purposes. Optical fibres are preferred over other conventional measuring techniques for such measurements due to its inherent advantages such as light weight, small size, low power, resistant to electromagnetic interference, high sensitivity, wide bandwidth, multiplexing advantages, geometrical flexibility and environmental ruggedness. Moreover, with the ready availability of optoelectronic components, fabrication cost of fibre optic sensors has drastically come down in the last two decades. In this paper, we are presenting an intensity modulated fibre optic sensor which is low in cost and capable of continuously monitoring and measuring the volume of different types of liquids with high degree of repeatability which can be calibrated with level. Here the intensity modulated technique has been chosen for measuring liquid level as it is simple and of low cost.

sensor, Fibre optics; level measurement; intensity modulation

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[8] Pérez-Ocón, F., et al. "Fiber-optic liquid-level continuous gauge." Sensors and Actuators A: Physical 125.2 (2006): 124-132.
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Sarmistha Das, Rupjyoti Haloi, Kanak Chandra Sarma, "Measurement of Liquid level by Using A Novel Fibre Optic Sensor", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.144-149, 2016.

Authentication system is the mechanism of proving the access to authorize users only and to prevent the various security threats. In the various one time passwords based (OTP) systems are third party dependents such as mobile, emails etc. In this paper we will discuss about the problems associated with various authentication techniques and a proposed system which is independent of third party and highly secured.

Network Security, Authentication, Public key, Private key, Cryptography, Encrypted one time password(EOTP)

[1]. R . L. Rivest, A. Shamir and L. Adleman, "On Digital Signatures and Public Key Cryptosystems", Technical Memo 82, Laboratory for Computer Science, Massachusetts Institute of Technology, April 1970.
[2]. National Institute of Standards and Technology (NIST) U.S.Department of Commerce: Electronic Authentication Guideline-Information Security, Special Publication 800-63-1, December 8,
[3]. W. E. Burr, D. F. Dodson, W. T. Polk. ElectronicAuthentication Guideline. Technical Report 800-63, National Institute of Standards andTechnology,2008..
[4]. CA.Managing strong Authentication: A Guide to Creating an Effective Management System, 2007. Dwiti Pandya, Khushboo Ram Narayan, Sneha Thakkar,Tanvi Madhekar nad B.S. Thakare, “An Overview of Various Authentication Methods and Protocols” in International Journal of Computer Applications (0975 – 8887) Volume 131 – No.9, December2015.
[5]. Geetanjali Choudhury and Jainul Abudin, “Modified Secure Two Way Authentication System in Cloud Computing Using Encrypted One Time Password” International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4077-4080.
[6]. Jainul Abudin, Sanjay Kumar Keot, Geetanjali Malakar, Nita Moni Borah and Mustafizur Rahman, “Modified RSA Public Key Cryptosystem Using Two Key Pairs” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 3548-3550.

Amarjyoti Pathak, Sanjay Kumar Keot, Utpal Barmen, Panu Boro, "Authentication Mechanism using Encrypted One time Password (EOTP)", International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.150-153, 2016.